Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization
Autor(a) principal: | |
---|---|
Data de Publicação: | 2003 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do IEN |
Texto Completo: | http://carpedien.ien.gov.br:8080/handle/ien/2641 |
Resumo: | In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been successfully applied in the solution of such kind of problem. The goal of the optimization is to maximize the system's average availability for a given period of time, considering realistic features such as: i) aging effects on standby components during the tests; ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; iii) components have distinct test parameters (outage time, aging factors, etc.) and iv) tests are not necessarily periodic. In our experiments, which were made in a cluster comprised by 8 1-GHz personal computers, we could clearly observe gains not only in the computational time, which reduced linearly with the number of computers, but in the optimization outcome. |
id |
IEN_4fdfe87b9bbc11819d6a2a6718929b73 |
---|---|
oai_identifier_str |
oai:carpedien.ien.gov.br:ien/2641 |
network_acronym_str |
IEN |
network_name_str |
Repositório Institucional do IEN |
spelling |
Pereira, Cláudio Márcio Nascimento AbreuLapa, Celso Marcelo FranklinInstituto de Engenharia Nuclear2018-07-26T17:49:08Z2018-07-26T17:49:08Z2003-04http://carpedien.ien.gov.br:8080/handle/ien/2641Submitted by Marcele Costal de Castro (costalcastro@gmail.com) on 2018-07-26T17:49:08Z No. of bitstreams: 0Made available in DSpace on 2018-07-26T17:49:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2003-04In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been successfully applied in the solution of such kind of problem. The goal of the optimization is to maximize the system's average availability for a given period of time, considering realistic features such as: i) aging effects on standby components during the tests; ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; iii) components have distinct test parameters (outage time, aging factors, etc.) and iv) tests are not necessarily periodic. In our experiments, which were made in a cluster comprised by 8 1-GHz personal computers, we could clearly observe gains not only in the computational time, which reduced linearly with the number of computers, but in the optimization outcome.engInstituto de Engenharia NuclearIENEstados unidosGenetic algorithmsParallel computationSurveillance Tests Policy OptimizationParallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimizationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Institucional do IENinstname:Instituto de Engenharia Nuclearinstacron:IENLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/2641/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ien/2641oai:carpedien.ien.gov.br:ien/26412018-07-26 14:49:08.326Dspace IENlsales@ien.gov.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 |
dc.title.pt_BR.fl_str_mv |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
title |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
spellingShingle |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization Pereira, Cláudio Márcio Nascimento Abreu Genetic algorithms Parallel computation Surveillance Tests Policy Optimization |
title_short |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
title_full |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
title_fullStr |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
title_full_unstemmed |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
title_sort |
Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization |
author |
Pereira, Cláudio Márcio Nascimento Abreu |
author_facet |
Pereira, Cláudio Márcio Nascimento Abreu Lapa, Celso Marcelo Franklin Instituto de Engenharia Nuclear |
author_role |
author |
author2 |
Lapa, Celso Marcelo Franklin Instituto de Engenharia Nuclear |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Pereira, Cláudio Márcio Nascimento Abreu Lapa, Celso Marcelo Franklin Instituto de Engenharia Nuclear |
dc.subject.por.fl_str_mv |
Genetic algorithms Parallel computation Surveillance Tests Policy Optimization |
topic |
Genetic algorithms Parallel computation Surveillance Tests Policy Optimization |
dc.description.abstract.por.fl_txt_mv |
In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been successfully applied in the solution of such kind of problem. The goal of the optimization is to maximize the system's average availability for a given period of time, considering realistic features such as: i) aging effects on standby components during the tests; ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; iii) components have distinct test parameters (outage time, aging factors, etc.) and iv) tests are not necessarily periodic. In our experiments, which were made in a cluster comprised by 8 1-GHz personal computers, we could clearly observe gains not only in the computational time, which reduced linearly with the number of computers, but in the optimization outcome. |
description |
In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been successfully applied in the solution of such kind of problem. The goal of the optimization is to maximize the system's average availability for a given period of time, considering realistic features such as: i) aging effects on standby components during the tests; ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; iii) components have distinct test parameters (outage time, aging factors, etc.) and iv) tests are not necessarily periodic. In our experiments, which were made in a cluster comprised by 8 1-GHz personal computers, we could clearly observe gains not only in the computational time, which reduced linearly with the number of computers, but in the optimization outcome. |
publishDate |
2003 |
dc.date.issued.fl_str_mv |
2003-04 |
dc.date.accessioned.fl_str_mv |
2018-07-26T17:49:08Z |
dc.date.available.fl_str_mv |
2018-07-26T17:49:08Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
status_str |
publishedVersion |
format |
article |
dc.identifier.uri.fl_str_mv |
http://carpedien.ien.gov.br:8080/handle/ien/2641 |
url |
http://carpedien.ien.gov.br:8080/handle/ien/2641 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.publisher.none.fl_str_mv |
Instituto de Engenharia Nuclear |
dc.publisher.initials.fl_str_mv |
IEN |
dc.publisher.country.fl_str_mv |
Estados unidos |
publisher.none.fl_str_mv |
Instituto de Engenharia Nuclear |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do IEN instname:Instituto de Engenharia Nuclear instacron:IEN |
reponame_str |
Repositório Institucional do IEN |
collection |
Repositório Institucional do IEN |
instname_str |
Instituto de Engenharia Nuclear |
instacron_str |
IEN |
institution |
IEN |
bitstream.url.fl_str_mv |
http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/2641/1/license.txt |
bitstream.checksum.fl_str_mv |
8a4605be74aa9ea9d79846c1fba20a33 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
repository.name.fl_str_mv |
Dspace IEN |
repository.mail.fl_str_mv |
lsales@ien.gov.br |
_version_ |
1656026997119254528 |